


'''simple docstring'''







import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

 if is_vision_available():
  from transformers import MaskFormerImageProcessor

if is_vision_available():
 from PIL import Image


class 			lowerCAmelCase_    :





  """simple docstring"""



  def __init__(       self  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				str=2					,				SCREAMING_SNAKE_CASE__  :				str=True					,				SCREAMING_SNAKE_CASE__  :				List[Any]=False					,				SCREAMING_SNAKE_CASE__  :				str=1_0					,				SCREAMING_SNAKE_CASE__  :				int=3					,				SCREAMING_SNAKE_CASE__  :				Optional[int]=3_2 * 4					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any]=3_2 * 6					,				SCREAMING_SNAKE_CASE__  :				Any=4					,				SCREAMING_SNAKE_CASE__  :				Dict=3_2					,				):





       '''simple docstring'''

       __a   			= parent
       __a   			= batch_size
       __a   			= is_training
       __a   			= use_auxiliary_loss
       __a   			= num_queries
       __a   			= num_channels
       __a   			= min_size
       __a   			= max_size
       __a   			= num_labels
       __a   			= mask_feature_size

  def __a    (       self  :				Any    ):





       '''simple docstring'''

       __a   			= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
           snake_case_    )

       __a   			= torch.ones([self.batch_size, self.min_size, self.max_size]					,				device=snake_case_    )

       __a   			= (
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]					,				device=snake_case_    ) > 0.5
       ).float()
       __a   			= (torch.rand((self.batch_size, self.num_labels)					,				device=snake_case_    ) > 0.5).long()

       __a   			= self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels

  def __a    (       self  :				str    ):





       '''simple docstring'''

       return MaskFormerConfig.from_backbone_and_decoder_configs(
           backbone_config=SwinConfig(
               depths=[1, 1, 1, 1]					,				)					,				decoder_config=DetrConfig(
               decoder_ffn_dim=1_2_8					,				num_queries=self.num_queries					,				decoder_attention_heads=2					,				d_model=self.mask_feature_size					,				)					,				mask_feature_size=self.mask_feature_size					,				fpn_feature_size=self.mask_feature_size					,				num_channels=self.num_channels					,				num_labels=self.num_labels					,				)

  def __a    (       self  :				Optional[int]    ):





       '''simple docstring'''

       __a   			= self.prepare_config_and_inputs()
       __a   			= {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
       return config, inputs_dict

  def __a    (       self  :				List[str]					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any]					,				SCREAMING_SNAKE_CASE__  :				List[Any]    ):





       '''simple docstring'''

       __a   			= output.encoder_hidden_states
       __a   			= output.pixel_decoder_hidden_states
       __a   			= output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(snake_case_    )					,				len(config.backbone_config.depths    )    )
       self.parent.assertTrue(len(snake_case_    )					,				len(config.backbone_config.depths    )    )
       self.parent.assertTrue(len(snake_case_    )					,				config.decoder_config.decoder_layers    )

  def __a    (       self  :				str					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any]					,				SCREAMING_SNAKE_CASE__  :				List[str]					,				SCREAMING_SNAKE_CASE__  :				int					,				SCREAMING_SNAKE_CASE__  :				str=False    ):





       '''simple docstring'''

       with torch.no_grad():
            __a   			= MaskFormerModel(config=snake_case_    )
            model.to(snake_case_    )
            model.eval()

            __a   			= model(pixel_values=snake_case_					,				pixel_mask=snake_case_    )
            __a   			= model(snake_case_					,				output_hidden_states=snake_case_    )
       # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
       # encoder and pixel decoder
       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape					,				(self.batch_size, self.num_queries, self.mask_feature_size)					,				)
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None    )
       self.parent.assertTrue(output.encoder_last_hidden_state is not None    )

       if output_hidden_states:
            self.check_output_hidden_state(snake_case_					,				snake_case_    )


  def __a    (       self  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Dict					,				SCREAMING_SNAKE_CASE__  :				List[str]					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				List[str]    ):





       '''simple docstring'''

       __a   			= MaskFormerForInstanceSegmentation(config=snake_case_    )
       model.to(snake_case_    )
       model.eval()

       def comm_check_on_output(SCREAMING_SNAKE_CASE__  :				List[Any]    ):
            # let's still check that all the required stuff is there
            self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None    )
            self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None    )
            self.parent.assertTrue(result.encoder_last_hidden_state is not None    )
            # okay, now we need to check the logits shape
            # due to the encoder compression, masks have a //4 spatial size
            self.parent.assertEqual(
                result.masks_queries_logits.shape					,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)					,				)
            # + 1 for null class
            self.parent.assertEqual(
                result.class_queries_logits.shape					,				(self.batch_size, self.num_queries, self.num_labels + 1)    )

       with torch.no_grad():
            __a   			= model(pixel_values=snake_case_					,				pixel_mask=snake_case_    )
            __a   			= model(snake_case_    )

            comm_check_on_output(snake_case_    )

            __a   			= model(
                pixel_values=snake_case_					,				pixel_mask=snake_case_					,				mask_labels=snake_case_					,				class_labels=snake_case_    )

       comm_check_on_output(snake_case_    )

       self.parent.assertTrue(result.loss is not None    )
       self.parent.assertEqual(result.loss.shape					,				torch.Size([1]    )    )


@require_torch
class 			lowerCAmelCase_    (							_A		,     _A		,     unittest.TestCase   ):





  """simple docstring"""



  a_							:Optional[int]				       =(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
  a_							:List[str]				       =(
      {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
      if is_torch_available()
      else {}
  )

  a_							:Union[str, Any]				       =False
  a_							:Any				       =False
  a_							:List[Any]				       =False
  a_							:int				       =False

  def __a    (       self  :				int    ):





       '''simple docstring'''

       __a   			= MaskFormerModelTester(self    )
       __a   			= ConfigTester(self					,				config_class=snake_case_					,				has_text_modality=snake_case_    )

  def __a    (       self  :				List[str]    ):





       '''simple docstring'''

       self.config_tester.run_common_tests()

  def __a    (       self  :				Tuple    ):





       '''simple docstring'''

       __a   			= self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(snake_case_					,				**snake_case_					,				output_hidden_states=snake_case_    )

  def __a    (       self  :				Dict    ):





       '''simple docstring'''

       __a   			= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_    )

  @unittest.skip(reason="""MaskFormer does not use inputs_embeds"""    )
  def __a    (       self  :				Union[str, Any]    ):





       '''simple docstring'''

       pass

  @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""    )
  def __a    (       self  :				Dict    ):





       '''simple docstring'''

       pass

  @unittest.skip(reason="""MaskFormer is not a generative model"""    )
  def __a    (       self  :				Any    ):





       '''simple docstring'''

       pass

  @unittest.skip(reason="""MaskFormer does not use token embeddings"""    )
  def __a    (       self  :				str    ):





       '''simple docstring'''

       pass

  @require_torch_multi_gpu
  @unittest.skip(
      reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""    )
  def __a    (       self  :				Any    ):





       '''simple docstring'''

       pass

  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
  def __a    (       self  :				Tuple    ):





       '''simple docstring'''

       pass

  def __a    (       self  :				List[str]    ):





       '''simple docstring'''

       __a   			= self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __a   			= model_class(snake_case_    )
            __a   			= inspect.signature(model.forward    )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __a   			= [*signature.parameters.keys()]

            __a   			= ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]					,				snake_case_    )

  @slow
  def __a    (       self  :				Dict    ):





       '''simple docstring'''

       for model_name in ["facebook/maskformer-swin-small-coco"]:
            __a   			= MaskFormerModel.from_pretrained(snake_case_    )
            self.assertIsNotNone(snake_case_    )

  def __a    (       self  :				str    ):





       '''simple docstring'''

       __a   			= (self.model_tester.min_size,) * 2
       __a   			= {
           """pixel_values""": torch.randn((2, 3, *size)					,				device=snake_case_    ),
           """mask_labels""": torch.randn((2, 1_0, *size)					,				device=snake_case_    ),
           """class_labels""": torch.zeros(2					,				1_0					,				device=snake_case_    ).long(),
       }

       __a   			= MaskFormerForInstanceSegmentation(MaskFormerConfig()    ).to(snake_case_    )
       __a   			= model(**snake_case_    )
       self.assertTrue(outputs.loss is not None    )

  def __a    (       self  :				List[str]    ):





       '''simple docstring'''

       __a   			= self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskformer_model(snake_case_					,				**snake_case_					,				output_hidden_states=snake_case_    )

  def __a    (       self  :				int    ):





       '''simple docstring'''

       __a   			= self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            __a   			= model_class(snake_case_    ).to(snake_case_    )
            __a   			= model(**snake_case_					,				output_attentions=snake_case_    )
            self.assertTrue(outputs.attentions is not None    )

  def __a    (       self  :				Optional[int]    ):





       '''simple docstring'''

       if not self.model_tester.is_training:
            return
       # only MaskFormerForInstanceSegmentation has the loss
       __a   			= self.all_model_classes[1]
       __a   			= self.model_tester.prepare_config_and_inputs()

       __a   			= model_class(snake_case_    )
       model.to(snake_case_    )
       model.train()

       __a   			= model(snake_case_					,				mask_labels=snake_case_					,				class_labels=snake_case_    ).loss
       loss.backward()


  def __a    (       self  :				Tuple    ):





       '''simple docstring'''

       __a   			= self.all_model_classes[1]
       __a   			= self.model_tester.prepare_config_and_inputs()
       __a   			= True
       __a   			= True

       __a   			= model_class(snake_case_    )
       model.to(snake_case_    )
       model.train()

       __a   			= model(snake_case_					,				mask_labels=snake_case_					,				class_labels=snake_case_    )

       __a   			= outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       __a   			= outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()
       # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
       __a   			= outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       __a   			= outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=snake_case_    )

       self.assertIsNotNone(encoder_hidden_states.grad    )
       self.assertIsNotNone(pixel_decoder_hidden_states.grad    )
       self.assertIsNotNone(transformer_decoder_hidden_states.grad    )
       self.assertIsNotNone(attentions.grad    )


SCREAMING_SNAKE_CASE_   =      1E-4




def 							__lowercase    (   )					->       List[str]:
     """simple docstring"""




     __a   			= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     return image



@require_vision
@slow
class 			lowerCAmelCase_    (							unittest.TestCase   ):





  """simple docstring"""



  @cached_property
  def __a    (       self  :				Tuple    ):





       '''simple docstring'''

       return (
           MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""    )
           if is_vision_available()
           else None
       )

  def __a    (       self  :				List[Any]    ):





       '''simple docstring'''

       __a   			= MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""    ).to(snake_case_    )
       __a   			= self.default_image_processor
       __a   			= prepare_img()
       __a   			= image_processor(snake_case_					,				return_tensors="""pt"""    ).to(snake_case_    )
       __a   			= inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
       # check size
       self.assertEqual(snake_case_					,				(1, 3, 8_0_0, 1_0_8_8)    )

       with torch.no_grad():
            __a   			= model(**snake_case_    )

       __a   			= torch.tensor(
           [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]    ).to(snake_case_    )
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]					,				snake_case_					,				atol=snake_case_    )    )

       __a   			= torch.tensor(
           [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]    ).to(snake_case_    )
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]					,				snake_case_					,				atol=snake_case_    )    )

       __a   			= torch.tensor(
           [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]    ).to(snake_case_    )
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]					,				snake_case_					,				atol=snake_case_    )    )

  def __a    (       self  :				Union[str, Any]    ):





       '''simple docstring'''

       __a   			= (
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""    )
           .to(snake_case_    )
           .eval()
       )
       __a   			= self.default_image_processor
       __a   			= prepare_img()
       __a   			= image_processor(snake_case_					,				return_tensors="""pt"""    ).to(snake_case_    )
       __a   			= inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
       # check size
       self.assertEqual(snake_case_					,				(1, 3, 8_0_0, 1_0_8_8)    )

       with torch.no_grad():
            __a   			= model(**snake_case_    )
       # masks_queries_logits
       __a   			= outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape					,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)					,				)
       __a   			= [
           [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
           [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
           [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
       ]
       __a   			= torch.tensor(snake_case_    ).to(snake_case_    )
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,				snake_case_					,				atol=snake_case_    )    )
       # class_queries_logits
       __a   			= outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape					,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
       __a   			= torch.tensor(
           [
               [1.65_12E00, -5.25_72E00, -3.35_19E00],
               [3.61_69E-02, -5.90_25E00, -2.93_13E00],
               [1.07_66E-04, -7.76_30E00, -5.12_63E00],
           ]    ).to(snake_case_    )
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,				snake_case_					,				atol=snake_case_    )    )

  def __a    (       self  :				List[Any]    ):





       '''simple docstring'''

       __a   			= (
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""    )
           .to(snake_case_    )
           .eval()
       )
       __a   			= self.default_image_processor
       __a   			= prepare_img()
       __a   			= image_processor(snake_case_					,				return_tensors="""pt"""    ).to(snake_case_    )
       __a   			= inputs["""pixel_values"""].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
       # check size
       self.assertEqual(snake_case_					,				(1, 3, 8_0_0, 1_0_8_8)    )

       with torch.no_grad():
            __a   			= model(**snake_case_    )
       # masks_queries_logits
       __a   			= outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape					,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)					,				)
       __a   			= [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
       __a   			= torch.tensor(snake_case_    ).to(snake_case_    )
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,				snake_case_					,				atol=snake_case_    )    )
       # class_queries_logits
       __a   			= outputs.class_queries_logits
       self.assertEqual(
           class_queries_logits.shape					,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
       __a   			= torch.tensor(
           [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]    ).to(snake_case_    )
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,				snake_case_					,				atol=snake_case_    )    )


  def __a    (       self  :				Tuple    ):





       '''simple docstring'''

       __a   			= (
           MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""    )
           .to(snake_case_    )
           .eval()
       )
       __a   			= self.default_image_processor

       __a   			= image_processor(
           [np.zeros((3, 8_0_0, 1_3_3_3)    ), np.zeros((3, 8_0_0, 1_3_3_3)    )]					,				segmentation_maps=[np.zeros((3_8_4, 3_8_4)    ).astype(np.floataa    ), np.zeros((3_8_4, 3_8_4)    ).astype(np.floataa    )]					,				return_tensors="""pt"""					,				)

       __a   			= inputs["""pixel_values"""].to(snake_case_    )
       __a   			= [el.to(snake_case_    ) for el in inputs["""mask_labels"""]]
       __a   			= [el.to(snake_case_    ) for el in inputs["""class_labels"""]]

       with torch.no_grad():
            __a   			= model(**snake_case_    )

       self.assertTrue(outputs.loss is not None    )








from ..utils import is_flax_available, is_torch_available


if is_torch_available():
							from .autoencoder_kl import AutoencoderKL
							from .controlnet import ControlNetModel
							from .dual_transformer_ad import DualTransformeraDModel
							from .modeling_utils import ModelMixin
							from .prior_transformer import PriorTransformer
							from .ta_film_transformer import TaFilmDecoder
							from .transformer_ad import TransformeraDModel
							from .unet_ad import UNetaDModel
							from .unet_ad import UNetaDModel
							from .unet_ad_condition import UNetaDConditionModel
							from .unet_ad_condition import UNetaDConditionModel
							from .vq_model import VQModel

if is_flax_available():
							from .controlnet_flax import FlaxControlNetModel
							from .unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .vae_flax import FlaxAutoencoderKL






